2.0 Test on MNIST

In [1]:
from clustergrammer_widget import *
net = Network(clustergrammer_widget)
df = {}

import clustergrammer_groupby as cby
import gene_exp_10x
In [2]:
from sklearn.metrics import f1_score
import pandas as pd
import numpy as np
from copy import deepcopy

import matplotlib.pyplot as plt
%matplotlib inline
In [3]:
net.load_file('../data/mnist.txt')
net.normalize(axis='row', norm_type='zscore')
df['ini'] = net.export_df()
df['ini'].shape
Out[3]:
(784, 200)
In [4]:
net.load_df(df['ini'])
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Zero', inst_color='yellow')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: One', inst_color='red')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Two', inst_color='blue')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Three', inst_color='green')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Four', inst_color='black')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Five', inst_color='orange')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Six', inst_color='purple')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Seven', inst_color='grey')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Eight', inst_color='white')
net.set_cat_color(axis='col', cat_index=1, cat_name='Digit: Nine', inst_color='brown')
In [5]:
net.load_df(df['ini'])
net.cluster()
net.widget()

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In [6]:
pval_cutoff = 0.001
df['cat_sig'], keep_genes, keep_genes_dict = cby.generate_signatures(df['ini'], 'Digit', 
                                                                     pval_cutoff=pval_cutoff)
print(df['cat_sig'].shape)
df['pred_cat'], df['sig_sim'], y_info = cby.predict_cats_from_sigs(df['ini'], df['cat_sig'], 
                                                                                  predict_level='Predict Digit')

df['conf'], populations, ser_correct, fraction_correct = cby.confusion_matrix_and_correct_series(y_info)
real_fraction_correct = deepcopy(fraction_correct)
print(real_fraction_correct)

(396, 10)
0.89
In [7]:
df['cat_sig'].shape
Out[7]:
(396, 10)
In [8]:
net.load_df(df['cat_sig'])
net.cluster()
net.widget()

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View Predictions

In [9]:
net.load_df(df['pred_cat'])
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Zero', inst_color='yellow')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: One', inst_color='red')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Two', inst_color='blue')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Three', inst_color='green')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Four', inst_color='black')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Five', inst_color='orange')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Six', inst_color='purple')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Seven', inst_color='grey')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Eight', inst_color='white')
net.set_cat_color(axis='col', cat_index=2, cat_name='Predict Digit: Nine', inst_color='brown')
In [10]:
net.load_df(df['pred_cat'].ix[keep_genes])
net.cluster()
net.widget()

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In [11]:
# net.load_df(df['sig_max'])
# net.cluster()
# net.widget()
In [12]:
net.load_df(df['conf'])
net.cluster()
net.widget()

Widget Javascript not detected.  It may not be installed or enabled properly.
In [13]:
ser_correct.sort_values(ascending=False).plot(kind='bar', figsize=(20,5), grid=True)
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x122774828>

Shuffle

In [14]:
%%time
num_shuffles = 100
perform_ser = cby.compare_performance_to_shuffled_labels(df['ini'], df['cat_sig'], 'Digit', 
                                                         num_shuffles=num_shuffles, pval_cutoff=pval_cutoff)
print('mean: ', perform_ser.mean(), 'std: ', perform_ser.std())

performance (fraction correct) of unshuffled: 0.89
mean:  0.23364999999999994 std:  0.041064466440469
CPU times: user 6.6 s, sys: 86.6 ms, total: 6.68 s
Wall time: 6.73 s
In [15]:
real_performance = perform_ser[perform_ser > real_fraction_correct].shape[0]/num_shuffles
print('real labels perform in the top ' + str(real_performance*100) + '% of shuffled labels')
print('previously calc real performance: ', real_fraction_correct)

real labels perform in the top 0.0% of shuffled labels
previously calc real performance:  0.89
In [16]:
perform_ser.hist()
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x122ad47f0>